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http://arks.princeton.edu/ark:/88435/dsp013r074x89zFull metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Berry, Michael | - |
| dc.contributor.author | Gaura, Alexander | - |
| dc.date.accessioned | 2020-07-24T12:30:59Z | - |
| dc.date.available | 2020-07-24T12:30:59Z | - |
| dc.date.created | 2020-05-12 | - |
| dc.date.issued | 2020-07-24 | - |
| dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp013r074x89z | - |
| dc.description.abstract | Traditional neural network models are based on ideas from neuroscience that have since become outdated. This paper investigates the performance of new neural network models that are based on modern neuroscience and compares their performance to other models on similar datasets. In particular, these models are useful for unsupervised learning, and differ most in how they receive input, their layering, and their learning rule which is based on Hebbian plasticity. | en_US |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en | en_US |
| dc.title | ORIGINAL | en_US |
| dc.title | ORIGINAL | en_US |
| dc.title | Brain-Based Machine Learning Algorithms | en_US |
| dc.type | Princeton University Senior Theses | - |
| pu.date.classyear | 2020 | en_US |
| pu.department | Mathematics | en_US |
| pu.pdf.coverpage | SeniorThesisCoverPage | - |
| pu.contributor.authorid | 920060731 | - |
| pu.certificate | Center for Statistics and Machine Learning | en_US |
| Appears in Collections: | Mathematics, 1934-2020 | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| GAURA-ALEXANDER-THESIS.pdf | 408.57 kB | Adobe PDF | Request a copy |
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